Measles coverage in Ho Chi Minh

library(tidyverse)
library(plotly)
library(downloadthis)

Download data

List of variables

Variable	Description
birth_year	birth year of cohort
district	residential address (24 districts of Ho Chi Minh city only)
any_shot	number of patients who were vaccinated at least 1 time
shots_1	number of patients who were vaccinated exactly 1 time
shots_2	number of patients who were vaccinated exactly 2 times
shots_3	number of patients who were vaccinated exactly 3 times
birth_count	number of newborns in birth_year (used as denominator for coverage)
coverage	proportion of newborns that were vaccinated for measles (i.e. any_shot/birth_count)
age_group	Either "[0-1)" or "all" if "[0-1)" then shot variables (any_shot, shots_1, etc.) only count vaccination shots within 1 year since birth if "[0-1)" then shot variables count all vaccination shots recorded in registry

</details>

Plot coverage over the year

Within one year of birth

ggplotly(
  ggplot() + 
    geom_line(
      aes(x = birth_year, y = coverage, color = district), 
      data = measles_coverage %>% filter(age_group == "[0-1)")
    )
)

All

ggplotly(
  ggplot() + 
    geom_line(
      aes(x = birth_year, y = coverage, color = district), 
      data = measles_coverage %>% filter(age_group == "all")
    )
)

Geo plot

import {addTooltips} from "@mkfreeman/plot-tooltip"
Plot = import("https://cdn.jsdelivr.net/npm/@observablehq/plot/+esm")
percent = d3.format(".2%")

district_map = FileAttachment("data/gadm41_VNM_2.json").json();
// only keep district of Ho Chi Minh city
district_map.features = district_map.features.filter(
  d => d.properties.NAME_1 == "HồChíMinh"
)

measles_coverage = FileAttachment("data/measles_coverage_data.csv").csv({typed: true});

viewof year_select = Inputs.range([2015, 2021], 
                                 {label: "Year of birth:", step: 1, value: 2021})
viewof vacage_group = Inputs.select(["[0-1)", "all"], {label: "Age at vaccination:"})

filtered_cov = measles_coverage.filter((d) => d.age_group === vacage_group & d.birth_year === year_select)

district_cov_map = new Map(
  filtered_cov.map( 
    // fix names to mix that of map data
  (d) => [
    d.district.normalize('NFD').replace(/[\u02c6-\u0323]/g, '')
    .replace("Đ", "D").replace("Quan", "District").toLowerCase().
    split(' ').join(''), 
  d.coverage] ))

addTooltips(
  Plot.plot({
    projection: {type: "mercator", domain: district_map},
    color: {
        scheme: "blues",
        label: "Coverage (%)",
        legend: true,
        percent: true, // Convert value to a percent (from a proportion)
        domain: [0, 100] // Update the value domain to span 0 to 100% access
      },
    marks: [
      Plot.geo(district_map, 
      {
        fill: (d) => district_cov_map.get(
          // update district name to match that in the registry for mapping
          d.properties.VARNAME_2.toLowerCase()
          ),
        title: (d) => `${d.properties.NAME_2}
                    ${percent(district_cov_map.get(d.properties.VARNAME_2.toLowerCase()))}`
      }),
      Plot.geo(district_map, {stroke: "black", strokeWidth: 0.5})
    ]
  })
)